OmniRAG-Agent Dataset

Training data for OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering [paper]

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Dataset Preview

Split	VideoOmniBench	WorldSense	Daily-Omni	Total
train	504	504	504	1,512

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Dataset Structure

OmniRAG-Agent/
├── VideoOmniBench/
│   └── train.parquet       # 504 samples — visual perception & reasoning
├── WorldSense/
│   └── train.parquet       # 504 samples — audio-visual understanding
└── Daily-Omni/
    └── train.parquet       # 504 samples — omnimodal event understanding

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Dataset Overview

Sub-dataset	Samples	Domain	Ability Focus
VideoOmniBench	504	Video QA	Counting, Temporal, Causal, Spatial, Ego Reasoning, etc.
WorldSense	504	Audio-Visual QA	Audio Recognition, Emotion, Spatial Relation, Event Sorting, etc.
Daily-Omni	504	Omnimodal Event QA	Event Sequence, AV Alignment, Inference, Reasoning, etc.

All samples are 4-choice multiple-choice questions (A/B/C/D) over short video clips (downsampled at 5-second intervals).

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Data Format

Each parquet file contains the following fields:

{
  "id": "video_109__q0000",
  "data_source": "OmniVideoBench",
  "prompt": [
    {
      "role": "user",
      "content": "<audio> <video>How many athletes participating in the race were shown in the video?"
    }
  ],
  "images": [],
  "audios": [],
  "videos": [
    {
      "type": "video",
      "video": "data/videoomnibench/videos_downsampled/video_109_t5s.mp4"
    }
  ],
  "fps": 0,
  "max_frames": 0,
  "min_frames": 0,
  "ability": "counting",
  "target": "D",
  "style": "",
  "answer": "5",
  "options": ["A.2", "B.3", "C.4", "D.5"],
  "correct_option": "D",
  "index": 0,
  "original_id": "video_109__q0000",
  "question": "How many athletes participating in the race were shown in the video?",
  "split": "train"
}

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Field Descriptions

Field	Type	Description
id	string	Unique sample identifier (<video_id>__<question_id>)
data_source	string	Source dataset name (always OmniVideoBench)
prompt	list[dict]	Conversation turns with role and content; content includes <audio> and <video> placeholders
images	list	Reserved for image inputs (empty in current split)
audios	list	Reserved for standalone audio inputs (audio is embedded in video)
videos	list[dict]	Video input: {"type": "video", "video": "<relative_path>"}
fps	int	Frame sampling rate (0 = use model default)
max_frames	int	Maximum frames to sample (0 = use model default)
min_frames	int	Minimum frames to sample (0 = use model default)
ability	string	Fine-grained ability category (see below)
target	string	Correct answer letter: A, B, C, or D
style	string	Question style tag (reserved)
answer	string	Ground-truth answer text
options	list[str]	Four answer choices, e.g. ["A.xxx", "B.xxx", "C.xxx", "D.xxx"]
correct_option	string	Correct option letter (A/B/C/D)
index	int	Sample index within sub-dataset
original_id	string	Original ID from the source dataset
question	string	Plain-text question (without modality tokens)
split	string	Dataset split (train)

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Ability Categories

VideoOmniBench

Ability	Description
counting	Count entities or events in the video
fine-grained perception	Identify subtle visual details
ego reasoning	First-person perspective understanding
sentiment analysis	Detect sentiment or emotion from video
temporal understanding	Reason about time order and duration
causal reasoning	Infer cause-and-effect relationships
reference reasoning	Resolve co-references across frames
summarization	Summarize the overall video content
background & music understanding	Understand scene background and music
spatial understanding	Reason about spatial relationships

WorldSense

Ability	Description
Audio Recognition	Identify sounds, instruments, or speech
Event Recognition	Recognise what event is happening
Object Counting	Count objects visible or audible
Spatial Relation	Determine spatial positions
Audio Source Localization	Locate the source of a sound
Video Emotions	Infer overall emotional tone
Emotion Change	Detect change in emotion over time
Action Counting	Count repeated actions
Event Sorting	Order events chronologically
Audio Change	Detect changes in audio over time

Daily-Omni

Ability	Description
Event Sequence	Order audio-visual events correctly
AV Event Alignment	Match audio events to visual moments
Inference	Infer implicit information
Reasoning	Multi-step logical reasoning
Context understanding	Understand overall context
Comparative	Compare attributes across clips

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Video File Structure

Videos are stored as downsampled 5-second clips (suffix _t5s.mp4) and referenced by relative paths:

data/
├── videoomnibench/
│   └── videos_downsampled/
│       └── video_<id>_t5s.mp4
├── WorldSense/
│   └── videos_downsampled/
│       └── <video_id>_t5s.mp4
└── Daily-Omni/
    └── videos_downsampled/
        └── <video_id>_video_t5s.mp4

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Usage

from datasets import load_dataset

# Load a single sub-dataset
vob = load_dataset(
    "parquet",
    data_files="VideoOmniBench/train.parquet",
    split="train"
)

# Load all three sub-datasets and concatenate
from datasets import concatenate_datasets

splits = ["VideoOmniBench", "WorldSense", "Daily-Omni"]
datasets = [
    load_dataset("parquet", data_files=f"{s}/train.parquet", split="train")
    for s in splits
]
full_dataset = concatenate_datasets(datasets)
print(f"Total samples: {len(full_dataset)}")   # 1512

Accessing a sample

sample = vob[0]
print(sample["question"])        # plain-text question
print(sample["options"])         # ['A.xxx', 'B.xxx', 'C.xxx', 'D.xxx']
print(sample["correct_option"])  # 'D'
print(sample["videos"][0]["video"])  # relative video path

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Evaluation Metric

All sub-datasets use multiple-choice accuracy (exact match of the predicted option letter against correct_option) as the primary evaluation metric.

Metric	Description
Accuracy	correct_option exact match
Per-ability Accuracy	Accuracy broken down by ability field

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Related Resources

• Paper: OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video QA
• RAG Server: FastAPI retrieval service (retrival_api/retriever.py) — exposes /query (image) and /query_audio (audio) endpoints
• Training Script: examples/grpo_trainer/run_omni_searchqa.sh — GRPO training with Qwen2.5-Omni-3B
• Evaluation Script: omni_batch_eval.py — agent-loop batch evaluation

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Citation

@article{omnirag2025,
  title  = {OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering},
  year   = {2025},
  url    = {https://arxiv.org/abs/2602.03707}
}